#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI图片内容分析模块
使用Claude Code的能力分析图片内容
"""

import os
import json
import base64
from pathlib import Path
from typing import Dict, List, Optional


class AIImageAnalyzer:
    """AI图片内容分析器"""
    
    def __init__(self):
        """初始化分析器"""
        self.analysis_cache = {}
        self.cache_file = "image_analysis_cache.json"
        self.load_cache()
    
    def load_cache(self):
        """加载分析缓存"""
        if os.path.exists(self.cache_file):
            try:
                with open(self.cache_file, 'r', encoding='utf-8') as f:
                    self.analysis_cache = json.load(f)
            except Exception as e:
                print(f"加载缓存失败: {e}")
                self.analysis_cache = {}
    
    def save_cache(self):
        """保存分析缓存"""
        try:
            with open(self.cache_file, 'w', encoding='utf-8') as f:
                json.dump(self.analysis_cache, f, ensure_ascii=False, indent=2)
        except Exception as e:
            print(f"保存缓存失败: {e}")
    
    def analyze_image_content(self, image_path: str) -> str:
        """
        分析图片内容
        
        Args:
            image_path: 图片文件路径
            
        Returns:
            图片内容描述
        """
        image_path = str(Path(image_path).resolve())
        
        # 检查缓存
        if image_path in self.analysis_cache:
            return self.analysis_cache[image_path]
        
        if not os.path.exists(image_path):
            return f"图片文件不存在: {image_path}"
        
        try:
            # 使用Claude Code分析图片
            # 这里模拟AI分析过程，实际使用时可以调用Read工具
            analysis = self._simulate_ai_analysis(image_path)
            
            # 缓存结果
            self.analysis_cache[image_path] = analysis
            self.save_cache()
            
            return analysis
            
        except Exception as e:
            error_msg = f"图片分析失败: {e}"
            print(error_msg)
            return error_msg
    
    def _simulate_ai_analysis(self, image_path: str) -> str:
        """
        模拟AI图片分析
        实际使用时可以替换为真实的AI分析调用
        
        Args:
            image_path: 图片路径
            
        Returns:
            分析结果
        """
        filename = Path(image_path).name
        
        # 根据文件名推测可能的内容类型
        analysis_templates = {
            'flow': "这是一个业务流程图，描述了系统的工作流程和各个步骤之间的关系。",
            'diagram': "这是一个系统架构图或关系图，展示了各个组件之间的连接和交互。",
            'chart': "这是一个图表，可能包含数据统计、比较分析或趋势展示。",
            'interface': "这是一个用户界面截图，展示了系统的功能页面和操作界面。",
            'table': "这是一个表格或数据展示，包含了结构化的信息和数据。"
        }
        
        # 简单的内容推测逻辑
        lower_filename = filename.lower()
        if any(keyword in lower_filename for keyword in ['flow', '流程', 'process']):
            base_desc = analysis_templates['flow']
        elif any(keyword in lower_filename for keyword in ['diagram', '图', 'chart']):
            base_desc = analysis_templates['diagram']
        elif any(keyword in lower_filename for keyword in ['interface', '界面', 'ui']):
            base_desc = analysis_templates['interface']
        elif any(keyword in lower_filename for keyword in ['table', '表格', 'data']):
            base_desc = analysis_templates['table']
        else:
            base_desc = "这是一个图片，包含了与预选供应商推荐线上化系统相关的信息。"
        
        return f"""**图片内容分析** ({filename}):

{base_desc}

*说明: 此分析为基于文件名的初步推测。在实际使用中，建议使用AI视觉模型进行详细的图片内容分析。*

---"""
    
    def batch_analyze_images(self, image_paths: List[str]) -> Dict[str, str]:
        """
        批量分析图片
        
        Args:
            image_paths: 图片路径列表
            
        Returns:
            图片路径到分析结果的映射
        """
        results = {}
        
        for image_path in image_paths:
            print(f"分析图片: {Path(image_path).name}")
            results[image_path] = self.analyze_image_content(image_path)
        
        return results


def create_ai_enhanced_converter():
    """创建集成AI分析的转换器类"""
    
    class AIEnhancedDocxToMarkdownConverter:
        """集成AI图片分析的Word转Markdown转换器"""
        
        def __init__(self, input_file: str, output_dir: str = None):
            # 导入原始转换器
            from docx_to_md_converter import DocxToMarkdownConverter
            
            # 继承原始功能
            self.base_converter = DocxToMarkdownConverter(input_file, output_dir)
            
            # 添加AI分析器
            self.ai_analyzer = AIImageAnalyzer()
        
        def analyze_image_content(self, image_path: str) -> str:
            """使用AI分析器分析图片内容"""
            return self.ai_analyzer.analyze_image_content(image_path)
        
        def convert(self) -> str:
            """执行转换，集成AI图片分析"""
            # 替换原始分析方法
            self.base_converter.analyze_image_content = self.analyze_image_content
            
            # 执行转换
            return self.base_converter.convert()
    
    return AIEnhancedDocxToMarkdownConverter


if __name__ == "__main__":
    # 测试AI图片分析器
    analyzer = AIImageAnalyzer()
    
    # 创建测试图片路径
    test_images = [
        "images/test_flow_diagram.png",
        "images/test_interface.jpg",
        "images/test_table.png"
    ]
    
    # 批量分析
    results = analyzer.batch_analyze_images(test_images)
    
    for image_path, analysis in results.items():
        print(f"\n{image_path}:")
        print(analysis)